Compensation Moneyball Part III: Making Use of Analyst Data

Pay-for-performance disconnects remain the top cause of shareholder backlash and say-on-pay problems. In response, compensation committees are asking for more proof that goals are sufficiently challenging.

Compensation Moneyball is a multi-part series about ways to deliver on this demand. Part I introduces the technique known as distribution fitting. Part II discusses another technique: Monte Carlo simulation.

Here, in part III of the Compensation Moneyball series, you’ll learn about yet another way to improve the goal-setting process: cross-referencing against analyst expectations. As a quantified reflection of market expectations of future performance, analyst reports are an objective resource for performance measurement. Get a crash course on these important influencers along with tips for leveraging analyst data in the goal-setting process.

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